Update app.py

app.py

@@ -24,10 +24,11 @@ def analyze_audio(audio_array, threshold=DEFAULT_THRESHOLD):
     - spectrogram visualization
     """
     try:
-        # Resample if needed and convert to mono
+        # Handle different audio input formats
         if isinstance(audio_array, tuple):
             sr, audio = audio_array
-            audio = librosa.resample(audio, orig_sr=sr, target_sr=SAMPLING_RATE)
+            if sr != SAMPLING_RATE:
+                audio = librosa.resample(audio, orig_sr=sr, target_sr=SAMPLING_RATE)
         else:
             audio = audio_array
             
@@ -57,17 +58,22 @@ def analyze_audio(audio_array, threshold=DEFAULT_THRESHOLD):
         spectrogram = librosa.feature.melspectrogram(
             y=audio, 
             sr=SAMPLING_RATE,
-            n_mels=128,
+            n_mels=64,  # Reduced from 128 to avoid warning
             fmax=8000
         )
         db_spec = librosa.power_to_db(spectrogram, ref=np.max)
         
-        plt.figure(figsize=(10, 4))
-        plt.imshow(db_spec, aspect='auto', origin='lower', 
-                  norm=Normalize(vmin=-80, vmax=0),
-                  cmap='viridis')
-        plt.colorbar(format='%+2.0f dB')
-        plt.title('Mel Spectrogram')
+        fig, ax = plt.subplots(figsize=(10, 4))
+        img = librosa.display.specshow(
+            db_spec, 
+            x_axis='time',
+            y_axis='mel',
+            sr=SAMPLING_RATE,
+            fmax=8000,
+            ax=ax
+        )
+        fig.colorbar(img, ax=ax, format='%+2.0f dB')
+        ax.set(title='Mel Spectrogram')
         plt.tight_layout()
         plt.savefig('spec.png', bbox_inches='tight')
         plt.close()
@@ -93,9 +99,7 @@ with gr.Blocks(title="Industrial Audio Analyzer", theme=gr.themes.Soft()) as dem
         with gr.Column():
             audio_input = gr.Audio(
                 label="Upload Equipment Audio Recording",
-                type="numpy",
-                source="upload",
-                show_download_button=True
+                type="numpy"
             )
             threshold = gr.Slider(
                 minimum=0.5,
@@ -107,12 +111,6 @@ with gr.Blocks(title="Industrial Audio Analyzer", theme=gr.themes.Soft()) as dem
             )
             analyze_btn = gr.Button("🔍 Analyze Sound", variant="primary")
             
-            gr.Examples(
-                examples=["examples/normal_machine.wav", "examples/anomalous_machine.wav"],
-                inputs=audio_input,
-                label="Sample Recordings"
-            )
-            
         with gr.Column():
             result_label = gr.Label(label="Detection Result")
             confidence = gr.Textbox(label="Confidence Score")